MTSANet: Multi-Head Two-Stream Attention Networks for Unsupervised Hyperspectral Image Super-Resolution

Jin Xu, Yuanchao Su, Lianru Gao, Xu Sun, Longfei Ren, Zhiqing Zhu, Mengying Jiang

Abstract: In recent years, deep learning has been proposed for hyperspectral images(HSIs) super-resolution, and many fusion models for HSI and multispectral images(MSIs) have been developed. However, these networks are constrained to the structure of convolutional neural networks(CNNs), and more attention needs to be paid to the disadvantage of the restricted receptive field of CNNs, such that some of the distal information needs to be included in the process of acquiring features. This approach involves capturing large-scale spatial features through multi-head spatial attention and spectral features of MSI and HSI through multi-head spectral attention. Subsequently, the features are processed by convolution kernels of different scales compactly. The effectiveness and competitiveness of MTSANet are evaluated by comparing it with some state-of-the-art (SOTA) methods.